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  • 1 month ago | bmcmededuc.biomedcentral.com | Naruporn Krungkraipetch |Luksanaporn Krungkraipetch |Sasikarnt Leelawongs

    Ophthalmology education is essential for equipping medical personnel to proficiently manage eye health concerns. This systematic review and meta-analysis evaluate ophthalmology education among medical students, medical schools, and physicians, identifying key gaps and areas for improvement. A systematic search of electronic databases (PubMed, Cochrane Library, Web of Science, and Scopus) identified studies published between January 2006 and December 2024. Eligible studies assessed competency levels, training duration, curriculum alignment, and compliance with ICO guidelines. The Preferred Reporting Items for the PRISMA statement were used for our reporting of the study selection process. Meta-analyses were conducted using random-effects and fixed-effects models, with heterogeneity assessed using I² statistics. Multiple regression analyses examined predictors of ophthalmology competency. PROSPERO registration number: CRD42024604742. Twelve studies from seven countries, involving 2,537 participants, were included. The pooled competency estimate was 62.5% (I² = 96.6%), with lower competency among medical students (55.8%) compared to physicians (83.3%). Training duration varied, with pooled estimates of 1.5 weeks for medical students, 2.5 weeks for medical schools, and 2.0 weeks for physicians. Curriculum alignment with ICO guidelines was 65% (I² = 96.2%), reflecting institutional variability. Compliance with ICO guidelines was also inconsistent (65%, I² = 99.2%). Multiple regression analysis identified competency level (β = 0.50, p < 0.001) and training duration (β = 0.42, p = 0.003) as the strongest predictors of ophthalmology education outcomes. Significant disparities exist in ophthalmology education. Standardized curricula, extended training, and enhanced practical skill development are needed to improve competency among medical trainees. Not applicable.

  • 1 month ago | bmcmededuc.biomedcentral.com | Kehinde O. Sunmboye |Samina Noorestani |Hannah Strafford |Malena Wilison-pirie

    With the integration of Artificial Intelligence (AI) into educational systems, its potential to revolutionize learning, particularly in content personalization and assessment support, is significant. Personalized learning, supported by AI tools, can adapt to individual learning styles and needs, thus transforming how medical students approach their studies. This study aims to explore the relationship between the use of AI for self-directed learning among undergraduate medical students in the UK and variables such as year of study, gender, and age. This cross-sectional study involved a sample of 230 undergraduate medical students from UK universities, collected through an online survey. The survey assessed AI usage in self-directed learning, including students’ attitudes towards AI accuracy, perceived benefits, and willingness to mitigate misinformation. Data were analyzed using descriptive statistics and linear logistic regression to examine associations between AI usage and demographics. The analysis revealed that age significantly influenced students’ willingness to pay for AI tools (p = 0.012) and gender was linked to concerns about AI inaccuracies (p = 0.017). Female students were more likely to take steps to mitigate risks of misinformation (p = 0.045). The study also found variability in AI usage based on the year of study, with first-year students showing a higher reliance on AI tools. AI has the potential to greatly enhance personalized learning for medical students. However, issues surrounding accuracy, misinformation, and equitable access need to be addressed to optimize AI integration in medical education. Further research is recommended to explore the longitudinal effects of AI usage on learning outcomes.

  • Mar 4, 2025 | bmcmededuc.biomedcentral.com | Jung-Hee Bae |Jae-Gi Lee |Ji-eun Im |Ja-young Gu

    In dental radiography education, students typically observe instructor demonstrations and practice on mannequins or peers. However, owing to the large student-to-instructor ratio, providing individualized feedback is challenging. Repeated practice is also hindered by radiation exposure from dental radiography machines. Implementing three-dimensional (3D) object-based virtual reality (VR) simulations can address these concerns. We developed a 3D object-based VR-simulation tool for dental radiography learning (namely, 3DOVR-DR) and evaluated user experiences. For the development of 3DOVR-DR, a virtual dental radiography room was constructed using 3D objects. The intraoral radiography process was divided into 12 steps, and the Unity 3D engine was used to create an interactive VR environment for step-by-step learning. This study was a randomized controlled trial. To evaluate user experience, 79 participants were randomly assigned to a control group (n = 39), which used Google Cardboard for VR, or an experimental group (n = 40), which used 3DOVR-DR, to evaluate the user experience. A survey questionnaire of 22 items was administered to all participants. Statistical analyses included descriptive statistics and Mann–Whitney U test. The 3DOVR-DR tool provided an immersive experience for simulating and learning the dental radiography process within a VR setting. Users performed step-by-step tasks related to dental radiography in the virtual space, adjusting and repeating the entire process or specific steps as needed for their learning. Users received guidance and practiced dental radiography using 3DOVR-DR. User-experience ratings were significantly higher in the experimental group (4.35±0.47) compared to the control group (3.63±0.66; P < 0.001). The 3DOVR-DR tool shows potential as a learning medium for intraoral radiography education. Further analysis is needed to examine the impact and mediating effects of the 3D object-based VR experience on dental radiographic practice. Future research should include pedagogical analysis to evaluate the educational effectiveness of this learning tool.

  • Feb 20, 2025 | bmcmededuc.biomedcentral.com | Sana Saeed |Sobia Ali |Azam Afzal |Marib Ghulam Rasool Malik |Muhammad Ahsan Naseer

    The integration of artificial intelligence (AI) into medical education is poised to revolutionize teaching, learning, and clinical practice. However, successful implementation of AI-based tools in medical curricula faces several challenges, particularly in resource-limited settings like Pakistan, where technological and institutional barriers remain significant. This study aimed to evaluate knowledge, attitudes, and practices of medical students and faculty regarding AI in medical education, and explore the perceptions and key barriers regarding strategies for effective AI integration. A concurrent mixed-methods study was conducted over six months (July 2023 to January 2024) at a tertiary care medical college in Pakistan. The quantitative component utilized a cross-sectional design, with 236 participants (153 medical students and 83 faculty members) completing an online survey. Mean composite scores for knowledge, attitudes, and practices were analyzed using non-parametric tests. The qualitative component consisted of three focus group discussions with students and six in-depth interviews with faculty. Thematic analysis was performed to explore participants’ perspectives on AI integration. Majority of participants demonstrated a positive attitude towards AI integration. Faculty had significantly higher mean attitude scores compared to students (3.95 ± 0.63 vs. 3.81 ± 0.75, p = 0.040). However, no statistically significant differences in knowledge (faculty: 3.53 ± 0.66, students: 3.55 ± 0.73, p = 0.870) or practices (faculty: 3.19 ± 0.87, students: 3.23 ± 0.89, p = 0.891) were found. Older students reported greater self-perceived knowledge (p = 0.010) and more positive attitudes (p = 0.016) towards AI, while male students exhibited higher knowledge scores than females (p = 0.025). Qualitative findings revealed key themes, including AI’s potential to enhance learning and research, concerns about over-reliance on AI, ethical issues surrounding privacy and confidentiality, and the need for institutional support. Faculty emphasized the importance of training to equip educators with the necessary skills to effectively integrate AI into their teaching. This study highlights both the enthusiasm for AI integration and the significant barriers that must be addressed to successfully implement AI in medical education. Addressing technological constraints, providing faculty training, and developing ethical guidelines are critical steps toward fostering the responsible use of AI in medical curricula. These findings underscore the need for context-specific strategies, particularly in resource-limited settings, to ensure that medical students and educators are well-prepared for the future of healthcare.

  • Feb 18, 2025 | bmcmededuc.biomedcentral.com | Stephanie Rupp |Benjamin Meindl |Klaus Schliz |Katharina Rädel-Ablass |Marion Roddewig |Cornelia Schlick | +3 more

    This study presents a novel approach to educational role-playing through an AI-based bot, leveraging GPT-4 to simulate anamnesis interviews in various learning scenarios. Developed collaboratively by an interdisciplinary team of university lecturers and AI experts, the bot provides a platform for students of different health study programs to engage in complex patient-health professional conversations, offering an alternative to traditional role plays with actors or real patients. This study utilized a GPT-4 based digital teaching assistant, implemented through a proprietary chatbot design platform, to train anamnesis interviews in virtual settings with students from different online health care study programs. Students’ satisfaction, virtual patient’s accuracy, its realism, and quality were evaluated with a quantitative survey. The evaluation of the bot focused on student feedback, highlighting a preference for the AI-driven method due to its immersive and interactive nature. Preliminary results show that students consistently rate the language ability of the AI model positively. More than 80% of students rated the professional and content-related precision of the virtual patient as good to excellent. Even as a text-based chatbot, the vast majority of students see a fairly close to very close relationship to a real anamnesis interview. The results further indicate that students even prefer this training approach to traditional in-person role-plays. The study underscores the bot’s potential as a versatile tool for enriching learning experiences across multiple health disciplines, signaling a meaningful shift in educational practices towards the integration of AI technologies.

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